Employee performance and retention analytics platform for a tech startup, reducing turnover by 40% and improving hiring success rates by 50%.
A fast-growing tech startup was experiencing high employee turnover (35% annually) and struggling with hiring decisions. HR lacked data-driven insights to identify at-risk employees and optimize recruitment processes.
They needed a comprehensive analytics platform to predict employee churn, improve hiring success rates, and create data-driven retention strategies.
Developed machine learning models analyzing performance data, engagement metrics, and survey responses to predict employee churn with 85% accuracy.
Built candidate scoring system using historical hiring data and performance outcomes to improve recruitment decision-making.
Created comprehensive HR dashboards tracking employee satisfaction, performance trends, and retention metrics for proactive management.
Implemented automated alerts for managers when employees show signs of disengagement or flight risk based on predictive models.
Decreased employee turnover from 35% to 21% through proactive retention strategies and early intervention programs.
Increased hiring success rate from 60% to 90% using data-driven candidate assessment and predictive scoring models.
Saved $280K annually in recruitment and training costs through improved retention and hiring effectiveness.
Improved employee satisfaction scores by 35% through data-driven insights and targeted engagement initiatives.